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Topic-aware Web Service Representation Learning

Authors:

Jianxun LiuAuthors Info & Claims

ACM Transactions on the Web (TWEB), Volume 14, Issue 2

Article No.: 9, Pages 1 - 23

https://doi.org/10.1145/3386041

Published: 11 April 2020 Publication History

Abstract

The advent of Service-Oriented Architecture (SOA) has brought a fundamental shift in the way in which distributed applications are implemented. An overwhelming number of Web-based services (e.g., APIs and Mashups) have leveraged this shift and furthered development. Applications designed with SOA principles are typically characterized by frequent dependencies with one another in the form of heterogeneous networks, i.e., annotation relations between tags and services, and composition relations between Mashups and APIs. Although prior work has shown the utility gained by exploring these networks, their analysis is still in its infancy. This article develops an approach to learning representations of the Web service network, which seeks to embed Web services in low-dimensional continuous vectors with preserved information of the network structure, functional tags, and service descriptions, such that services with similar functional properties and network structures are mapped together in the learned latent space. We first propose a topic generative model for constructing two topic distribution networks (Mashup-Topic and API-Topic) from the service content. Then, we present an efficient optimization process to derive low-dimensional vector representations of Web services from a tri-layer bipartite network with the Mashup-Topic and API-Topic networks on two ends and the Mashup-API composition network in the middle. Experiments on real-word datasets have verified that our approach is effective to learn robust low-rank service representations, i.e., 25% F1-measure gain over the state-of-the-art in Web service recommendation task.

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Qiao ZWang PWang PNing ZFu YDu YZhou YHuang JHua XXiong H(2024)A Dual-channel Semi-supervised Learning Framework on Graphs via Knowledge Transfer and Meta-learningACM Transactions on the Web10.1145/357703318:2(1-26)Online publication date: 8-Jan-2024
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Chen QKang GNie YLiu JCao B(2024)A General Complementary API Recommendation Framework based on Learning Model2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00048(258-268)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00048
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https://doi.org/10.1016/j.infsof.2024.107428
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Index Terms

Topic-aware Web Service Representation Learning

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cover image ACM Transactions on the Web

ACM Transactions on the Web Volume 14, Issue 2

May 2020

149 pages

ISSN:1559-1131

EISSN:1559-114X

DOI:10.1145/3382502

Editor:
Brian D. Davison
Lehigh University, USA

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Copyright © 2020 ACM.

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Association for Computing Machinery

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Publication History

Published: 11 April 2020

Accepted: 01 June 2009

Revised: 01 March 2009

Received: 01 February 2007

Published in TWEB Volume 14, Issue 2

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Cited By

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https://dl.acm.org/doi/10.1145/3577033
Chen QKang GNie YLiu JCao B(2024)A General Complementary API Recommendation Framework based on Learning Model2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00048(258-268)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00048
Tang MXie FLian SMai JLi S(2024)Mashup-oriented API recommendation via pre-trained heterogeneous information networksInformation and Software Technology10.1016/j.infsof.2024.107428169(107428)Online publication date: May-2024
https://doi.org/10.1016/j.infsof.2024.107428
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Peng QCao BXie XLiu SKang GLiu J(2023)TH-SLP: Web Service Link Prediction Based on Topic-aware Heterogeneous Graph Neural Network2023 IEEE International Conference on Web Services (ICWS)10.1109/ICWS60048.2023.00065(465-474)Online publication date: Jul-2023
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Shi MZhuang YTang YLin MZhu XLiu J(2022)Web Service Network Embedding Based on Link Prediction and Convolutional LearningIEEE Transactions on Services Computing10.1109/TSC.2021.310348115:6(3620-3633)Online publication date: 1-Nov-2022
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Agarwal NSikka GAwasthi L(2022)A systematic literature review on web service clustering approaches to enhance service discovery, selection and recommendationComputer Science Review10.1016/j.cosrev.2022.10049845(100498)Online publication date: Aug-2022
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